# VHS course "R"Kenntnisse um Wissen aus Daten zu gewinnen 2016
# Beispiel von Hadley Wickham https://www.youtube.com/watch?v=rz3_FDVt9eg
library(gapminder)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(purrr)
##
## Attaching package: 'purrr'
## The following objects are masked from 'package:dplyr':
##
## contains, order_by
library(tidyr)
library(ggplot2)
library(broom)
library(purrr)
library(magrittr)
##
## Attaching package: 'magrittr'
## The following object is masked from 'package:tidyr':
##
## extract
## The following object is masked from 'package:purrr':
##
## set_names
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
# Print xy plot -----------------------------------------------------------
gapminder
## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.801 8425333 779.4453
## 2 Afghanistan Asia 1957 30.332 9240934 820.8530
## 3 Afghanistan Asia 1962 31.997 10267083 853.1007
## 4 Afghanistan Asia 1967 34.020 11537966 836.1971
## 5 Afghanistan Asia 1972 36.088 13079460 739.9811
## 6 Afghanistan Asia 1977 38.438 14880372 786.1134
## 7 Afghanistan Asia 1982 39.854 12881816 978.0114
## 8 Afghanistan Asia 1987 40.822 13867957 852.3959
## 9 Afghanistan Asia 1992 41.674 16317921 649.3414
## 10 Afghanistan Asia 1997 41.763 22227415 635.3414
## # ... with 1,694 more rows
gapminder <- gapminder %>% mutate(year1950 = year -1950)
ggplot(gapminder, aes(x=year, y=lifeExp)) +geom_line(aes(group = country))
# Nested data -------------------------------------------------------------
by_country <- gapminder %>%
group_by(continent, country) %>%
nest()
by_country
## # A tibble: 142 × 3
## continent country data
## <fctr> <fctr> <list>
## 1 Asia Afghanistan <tibble [12 × 5]>
## 2 Europe Albania <tibble [12 × 5]>
## 3 Africa Algeria <tibble [12 × 5]>
## 4 Africa Angola <tibble [12 × 5]>
## 5 Americas Argentina <tibble [12 × 5]>
## 6 Oceania Australia <tibble [12 × 5]>
## 7 Europe Austria <tibble [12 × 5]>
## 8 Asia Bahrain <tibble [12 × 5]>
## 9 Asia Bangladesh <tibble [12 × 5]>
## 10 Europe Belgium <tibble [12 × 5]>
## # ... with 132 more rows
str(by_country)
## Classes 'tbl_df', 'tbl' and 'data.frame': 142 obs. of 3 variables:
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 4 1 1 2 5 4 3 3 4 ...
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ data :List of 142
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 28.8 30.3 32 34 36.1 ...
## .. ..$ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## .. ..$ gdpPercap: num 779 821 853 836 740 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 55.2 59.3 64.8 66.2 67.7 ...
## .. ..$ pop : int 1282697 1476505 1728137 1984060 2263554 2509048 2780097 3075321 3326498 3428038 ...
## .. ..$ gdpPercap: num 1601 1942 2313 2760 3313 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 43.1 45.7 48.3 51.4 54.5 ...
## .. ..$ pop : int 9279525 10270856 11000948 12760499 14760787 17152804 20033753 23254956 26298373 29072015 ...
## .. ..$ gdpPercap: num 2449 3014 2551 3247 4183 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 30 32 34 36 37.9 ...
## .. ..$ pop : int 4232095 4561361 4826015 5247469 5894858 6162675 7016384 7874230 8735988 9875024 ...
## .. ..$ gdpPercap: num 3521 3828 4269 5523 5473 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 62.5 64.4 65.1 65.6 67.1 ...
## .. ..$ pop : int 17876956 19610538 21283783 22934225 24779799 26983828 29341374 31620918 33958947 36203463 ...
## .. ..$ gdpPercap: num 5911 6857 7133 8053 9443 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 69.1 70.3 70.9 71.1 71.9 ...
## .. ..$ pop : int 8691212 9712569 10794968 11872264 13177000 14074100 15184200 16257249 17481977 18565243 ...
## .. ..$ gdpPercap: num 10040 10950 12217 14526 16789 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 66.8 67.5 69.5 70.1 70.6 ...
## .. ..$ pop : int 6927772 6965860 7129864 7376998 7544201 7568430 7574613 7578903 7914969 8069876 ...
## .. ..$ gdpPercap: num 6137 8843 10751 12835 16662 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 50.9 53.8 56.9 59.9 63.3 ...
## .. ..$ pop : int 120447 138655 171863 202182 230800 297410 377967 454612 529491 598561 ...
## .. ..$ gdpPercap: num 9867 11636 12753 14805 18269 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 37.5 39.3 41.2 43.5 45.3 ...
## .. ..$ pop : int 46886859 51365468 56839289 62821884 70759295 80428306 93074406 103764241 113704579 123315288 ...
## .. ..$ gdpPercap: num 684 662 686 721 630 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 68 69.2 70.2 70.9 71.4 ...
## .. ..$ pop : int 8730405 8989111 9218400 9556500 9709100 9821800 9856303 9870200 10045622 10199787 ...
## .. ..$ gdpPercap: num 8343 9715 10991 13149 16672 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 38.2 40.4 42.6 44.9 47 ...
## .. ..$ pop : int 1738315 1925173 2151895 2427334 2761407 3168267 3641603 4243788 4981671 6066080 ...
## .. ..$ gdpPercap: num 1063 960 949 1036 1086 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 40.4 41.9 43.4 45 46.7 ...
## .. ..$ pop : int 2883315 3211738 3593918 4040665 4565872 5079716 5642224 6156369 6893451 7693188 ...
## .. ..$ gdpPercap: num 2677 2128 2181 2587 2980 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 53.8 58.5 61.9 64.8 67.5 ...
## .. ..$ pop : int 2791000 3076000 3349000 3585000 3819000 4086000 4172693 4338977 4256013 3607000 ...
## .. ..$ gdpPercap: num 974 1354 1710 2172 2860 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 47.6 49.6 51.5 53.3 56 ...
## .. ..$ pop : int 442308 474639 512764 553541 619351 781472 970347 1151184 1342614 1536536 ...
## .. ..$ gdpPercap: num 851 918 984 1215 2264 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 50.9 53.3 55.7 57.6 59.5 ...
## .. ..$ pop : int 56602560 65551171 76039390 88049823 100840058 114313951 128962939 142938076 155975974 168546719 ...
## .. ..$ gdpPercap: num 2109 2487 3337 3430 4986 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 59.6 66.6 69.5 70.4 70.9 ...
## .. ..$ pop : int 7274900 7651254 8012946 8310226 8576200 8797022 8892098 8971958 8658506 8066057 ...
## .. ..$ gdpPercap: num 2444 3009 4254 5577 6597 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 32 34.9 37.8 40.7 43.6 ...
## .. ..$ pop : int 4469979 4713416 4919632 5127935 5433886 5889574 6634596 7586551 8878303 10352843 ...
## .. ..$ gdpPercap: num 543 617 723 795 855 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 39 40.5 42 43.5 44.1 ...
## .. ..$ pop : int 2445618 2667518 2961915 3330989 3529983 3834415 4580410 5126023 5809236 6121610 ...
## .. ..$ gdpPercap: num 339 380 355 413 464 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 39.4 41.4 43.4 45.4 40.3 ...
## .. ..$ pop : int 4693836 5322536 6083619 6960067 7450606 6978607 7272485 8371791 10150094 11782962 ...
## .. ..$ gdpPercap: num 368 434 497 523 422 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 38.5 40.4 42.6 44.8 47 ...
## .. ..$ pop : int 5009067 5359923 5793633 6335506 7021028 7959865 9250831 10780667 12467171 14195809 ...
## .. ..$ gdpPercap: num 1173 1313 1400 1508 1684 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 68.8 70 71.3 72.1 72.9 ...
## .. ..$ pop : int 14785584 17010154 18985849 20819767 22284500 23796400 25201900 26549700 28523502 30305843 ...
## .. ..$ gdpPercap: num 11367 12490 13462 16077 18971 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 35.5 37.5 39.5 41.5 43.5 ...
## .. ..$ pop : int 1291695 1392284 1523478 1733638 1927260 2167533 2476971 2840009 3265124 3696513 ...
## .. ..$ gdpPercap: num 1071 1191 1193 1136 1070 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 38.1 39.9 41.7 43.6 45.6 ...
## .. ..$ pop : int 2682462 2894855 3150417 3495967 3899068 4388260 4875118 5498955 6429417 7562011 ...
## .. ..$ gdpPercap: num 1179 1308 1390 1197 1104 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 54.7 56.1 57.9 60.5 63.4 ...
## .. ..$ pop : int 6377619 7048426 7961258 8858908 9717524 10599793 11487112 12463354 13572994 14599929 ...
## .. ..$ gdpPercap: num 3940 4316 4519 5107 5494 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 44 50.5 44.5 58.4 63.1 ...
## .. ..$ pop : int 556263527 637408000 665770000 754550000 862030000 943455000 1000281000 1084035000 1164970000 1230075000 ...
## .. ..$ gdpPercap: num 400 576 488 613 677 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 50.6 55.1 57.9 60 61.6 ...
## .. ..$ pop : int 12350771 14485993 17009885 19764027 22542890 25094412 27764644 30964245 34202721 37657830 ...
## .. ..$ gdpPercap: num 2144 2324 2492 2679 3265 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 40.7 42.5 44.5 46.5 48.9 ...
## .. ..$ pop : int 153936 170928 191689 217378 250027 304739 348643 395114 454429 527982 ...
## .. ..$ gdpPercap: num 1103 1211 1407 1876 1938 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 39.1 40.7 42.1 44.1 46 ...
## .. ..$ pop : int 14100005 15577932 17486434 19941073 23007669 26480870 30646495 35481645 41672143 47798986 ...
## .. ..$ gdpPercap: num 781 906 896 862 905 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 42.1 45.1 48.4 52 54.9 ...
## .. ..$ pop : int 854885 940458 1047924 1179760 1340458 1536769 1774735 2064095 2409073 2800947 ...
## .. ..$ gdpPercap: num 2126 2315 2465 2678 3213 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 57.2 60 62.8 65.4 67.8 ...
## .. ..$ pop : int 926317 1112300 1345187 1588717 1834796 2108457 2424367 2799811 3173216 3518107 ...
## .. ..$ gdpPercap: num 2627 2990 3461 4162 5118 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 40.5 42.5 44.9 47.4 49.8 ...
## .. ..$ pop : int 2977019 3300000 3832408 4744870 6071696 7459574 9025951 10761098 12772596 14625967 ...
## .. ..$ gdpPercap: num 1389 1501 1729 2052 2378 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 61.2 64.8 67.1 68.5 69.6 ...
## .. ..$ pop : int 3882229 3991242 4076557 4174366 4225310 4318673 4413368 4484310 4494013 4444595 ...
## .. ..$ gdpPercap: num 3119 4338 5478 6960 9164 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 59.4 62.3 65.2 68.3 70.7 ...
## .. ..$ pop : int 6007797 6640752 7254373 8139332 8831348 9537988 9789224 10239839 10723260 10983007 ...
## .. ..$ gdpPercap: num 5587 6092 5181 5690 5305 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 66.9 69 69.9 70.4 70.3 ...
## .. ..$ pop : int 9125183 9513758 9620282 9835109 9862158 10161915 10303704 10311597 10315702 10300707 ...
## .. ..$ gdpPercap: num 6876 8256 10137 11399 13108 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 70.8 71.8 72.3 73 73.5 ...
## .. ..$ pop : int 4334000 4487831 4646899 4838800 4991596 5088419 5117810 5127024 5171393 5283663 ...
## .. ..$ gdpPercap: num 9692 11100 13583 15937 18866 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 34.8 37.3 39.7 42.1 44.4 ...
## .. ..$ pop : int 63149 71851 89898 127617 178848 228694 305991 311025 384156 417908 ...
## .. ..$ gdpPercap: num 2670 2865 3021 3020 3694 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 45.9 49.8 53.5 56.8 59.6 ...
## .. ..$ pop : int 2491346 2923186 3453434 4049146 4671329 5302800 5968349 6655297 7351181 7992357 ...
## .. ..$ gdpPercap: num 1398 1544 1662 1654 2190 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 48.4 51.4 54.6 56.7 58.8 ...
## .. ..$ pop : int 3548753 4058385 4681707 5432424 6298651 7278866 8365850 9545158 10748394 11911819 ...
## .. ..$ gdpPercap: num 3522 3781 4086 4579 5281 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 41.9 44.4 47 49.3 51.1 ...
## .. ..$ pop : int 22223309 25009741 28173309 31681188 34807417 38783863 45681811 52799062 59402198 66134291 ...
## .. ..$ gdpPercap: num 1419 1459 1693 1815 2024 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 45.3 48.6 52.3 55.9 58.2 ...
## .. ..$ pop : int 2042865 2355805 2747687 3232927 3790903 4282586 4474873 4842194 5274649 5783439 ...
## .. ..$ gdpPercap: num 3048 3422 3777 4359 4520 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 34.5 36 37.5 39 40.5 ...
## .. ..$ pop : int 216964 232922 249220 259864 277603 192675 285483 341244 387838 439971 ...
## .. ..$ gdpPercap: num 376 426 583 916 672 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 35.9 38 40.2 42.2 44.1 ...
## .. ..$ pop : int 1438760 1542611 1666618 1820319 2260187 2512642 2637297 2915959 3668440 4058319 ...
## .. ..$ gdpPercap: num 329 344 381 469 514 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 34.1 36.7 40.1 42.1 43.5 ...
## .. ..$ pop : int 20860941 22815614 25145372 27860297 30770372 34617799 38111756 42999530 52088559 59861301 ...
## .. ..$ gdpPercap: num 362 379 419 516 566 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 66.5 67.5 68.8 69.8 70.9 ...
## .. ..$ pop : int 4090500 4324000 4491443 4605744 4639657 4738902 4826933 4931729 5041039 5134406 ...
## .. ..$ gdpPercap: num 6425 7545 9372 10922 14359 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 67.4 68.9 70.5 71.5 72.4 ...
## .. ..$ pop : int 42459667 44310863 47124000 49569000 51732000 53165019 54433565 55630100 57374179 58623428 ...
## .. ..$ gdpPercap: num 7030 8663 10560 13000 16107 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 37 39 40.5 44.6 48.7 ...
## .. ..$ pop : int 420702 434904 455661 489004 537977 706367 753874 880397 985739 1126189 ...
## .. ..$ gdpPercap: num 4293 4976 6631 8359 11402 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 30 32.1 33.9 35.9 38.3 ...
## .. ..$ pop : int 284320 323150 374020 439593 517101 608274 715523 848406 1025384 1235767 ...
## .. ..$ gdpPercap: num 485 521 600 735 756 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## .. ..$ lifeExp : num 67.5 69.1 70.3 70.8 71 ...
## .. ..$ pop : int 69145952 71019069 73739117 76368453 78717088 78160773 78335266 77718298 80597764 82011073 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## .. ..$ gdpPercap: num 911 1044 1190 1126 1178 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## .. ..$ lifeExp : num 65.9 67.9 69.5 71 72.3 ...
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## .. ..$ gdpPercap: num 3531 4916 6017 8513 12725 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
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## .. ..$ gdpPercap: num 2428 2617 2750 3243 4031 ...
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## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## .. ..$ gdpPercap: num 1840 1727 1797 1452 1654 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## .. ..$ lifeExp : num 64 66.4 68 69.5 69.8 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## .. ..$ pop : int 147962 165110 182053 198676 209275 221823 233997 244676 259012 271192 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## .. ..$ pop : int 372000000 409000000 454000000 506000000 567000000 634000000 708000000 788000000 872000000 959000000 ...
## .. ..$ gdpPercap: num 547 590 658 701 724 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## .. ..$ lifeExp : num 37.5 39.9 42.5 46 49.2 ...
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## .. ..$ gdpPercap: num 750 859 849 762 1111 ...
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## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 44.9 47.2 49.3 52.5 55.2 ...
## .. ..$ pop : int 17272000 19792000 22874000 26538000 30614000 35480679 43072751 51889696 60397973 63327987 ...
## .. ..$ gdpPercap: num 3035 3290 4187 5907 9614 ...
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## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
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## .. ..$ lifeExp : num 45.3 48.4 51.5 54.5 57 ...
## .. ..$ pop : int 5441766 6248643 7240260 8519282 10061506 11882916 14173318 16543189 17861905 20775703 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 66.9 68.9 70.3 71.1 71.3 ...
## .. ..$ pop : int 2952156 2878220 2830000 2900100 3024400 3271900 3480000 3539900 3557761 3667233 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 65.4 67.8 69.4 70.8 71.6 ...
## .. ..$ pop : int 1620914 1944401 2310904 2693585 3095893 3495918 3858421 4203148 4936550 5531387 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 65.9 67.8 69.2 71.1 72.2 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 58.5 62.6 65.6 67.5 69 ...
## .. ..$ pop : int 1426095 1535090 1665128 1861096 1997616 2156814 2298309 2326606 2378618 2531311 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 63 65.5 68.7 71.4 73.4 ...
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## .. ..$ gdpPercap: num 3217 4318 6577 9848 14779 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 50.1 54.1 56.7 59.9 64 ...
## .. ..$ pop : int 8865488 9411381 10917494 12617009 14781241 16325320 17647518 19067554 20711375 21585105 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ pop : int 160000 212846 358266 575003 841934 1140357 1497494 1891487 1418095 1765345 ...
## .. ..$ gdpPercap: num 108382 113523 95458 80895 109348 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ pop : int 1439529 1647412 1886848 2186894 2680018 3115787 3086876 3089353 3219994 3430388 ...
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## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ pop : int 1019729 1201578 1441863 1759224 2183877 2721783 3344074 3799845 4364501 4759670 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 48.5 52.1 55.7 59.4 63 ...
## .. ..$ pop : int 6748378 7739235 8906385 10154878 11441462 12845381 14441916 16331785 18319502 20476091 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ pop : int 3838168 4241884 4690372 5212416 5828158 6491649 6998256 7634008 8416215 9384984 ...
## .. ..$ gdpPercap: num 452 490 496 545 581 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ pop : int 516556 609816 701016 789309 851334 913025 992040 1042663 1096202 1149818 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ pop : int 800663 882134 1010280 1149500 1320500 1528000 1756032 2015133 2312802 2494803 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 42.9 45.4 47.9 50.3 52.9 ...
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## .. ..$ gdpPercap: num 1688 1642 1566 1711 1930 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ gdpPercap: num 331 350 388 349 357 371 424 385 347 415 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ gdpPercap: num 8942 11276 12791 15363 18795 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
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## .. ..$ gdpPercap: num 10557 12247 13176 14464 16046 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 42.3 45.4 48.6 51.9 55.2 ...
## .. ..$ pop : int 1165790 1358828 1590597 1865490 2182908 2554598 2979423 3344353 4017939 4609572 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 37.4 38.6 39.5 40.1 40.5 ...
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## .. ..$ gdpPercap: num 762 836 998 1054 954 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 36.3 37.8 39.4 41 42.8 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 72.7 73.4 73.5 74.1 74.3 ...
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## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 37.6 40.1 43.2 47 52.1 ...
## .. ..$ pop : int 507833 561977 628164 714775 829050 1004533 1301048 1593882 1915208 2283635 ...
## .. ..$ gdpPercap: num 1828 2243 2925 4721 10618 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 43.4 45.6 47.7 49.8 51.9 ...
## .. ..$ pop : int 41346560 46679944 53100671 60641899 69325921 78152686 91462088 105186881 120065004 135564834 ...
## .. ..$ gdpPercap: num 685 747 803 942 1050 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## ..$ :Classes 'tbl_df', 'tbl' and 'data.frame': 12 obs. of 5 variables:
## .. ..$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## .. ..$ lifeExp : num 55.2 59.2 61.8 64.1 66.2 ...
## .. ..$ pop : int 940080 1063506 1215725 1405486 1616384 1839782 2036305 2253639 2484997 2734531 ...
## .. ..$ gdpPercap: num 2480 2962 3537 4421 5364 ...
## .. ..$ year1950 : num 2 7 12 17 22 27 32 37 42 47 ...
## .. [list output truncated]
by_country$data[[1]]
## # A tibble: 12 × 5
## year lifeExp pop gdpPercap year1950
## <int> <dbl> <int> <dbl> <dbl>
## 1 1952 28.801 8425333 779.4453 2
## 2 1957 30.332 9240934 820.8530 7
## 3 1962 31.997 10267083 853.1007 12
## 4 1967 34.020 11537966 836.1971 17
## 5 1972 36.088 13079460 739.9811 22
## 6 1977 38.438 14880372 786.1134 27
## 7 1982 39.854 12881816 978.0114 32
## 8 1987 40.822 13867957 852.3959 37
## 9 1992 41.674 16317921 649.3414 42
## 10 1997 41.763 22227415 635.3414 47
## 11 2002 42.129 25268405 726.7341 52
## 12 2007 43.828 31889923 974.5803 57
# Fit models --------------------------------------------------------------
country_model <- function(df){
lm(lifeExp ~ year1950, data=df)
}
models <- by_country %>%
mutate(
model = map(data, country_model)
)
models
## # A tibble: 142 × 4
## continent country data model
## <fctr> <fctr> <list> <list>
## 1 Asia Afghanistan <tibble [12 × 5]> <S3: lm>
## 2 Europe Albania <tibble [12 × 5]> <S3: lm>
## 3 Africa Algeria <tibble [12 × 5]> <S3: lm>
## 4 Africa Angola <tibble [12 × 5]> <S3: lm>
## 5 Americas Argentina <tibble [12 × 5]> <S3: lm>
## 6 Oceania Australia <tibble [12 × 5]> <S3: lm>
## 7 Europe Austria <tibble [12 × 5]> <S3: lm>
## 8 Asia Bahrain <tibble [12 × 5]> <S3: lm>
## 9 Asia Bangladesh <tibble [12 × 5]> <S3: lm>
## 10 Europe Belgium <tibble [12 × 5]> <S3: lm>
## # ... with 132 more rows
models %>% filter(continent =="Africa")
## # A tibble: 52 × 4
## continent country data model
## <fctr> <fctr> <list> <list>
## 1 Africa Algeria <tibble [12 × 5]> <S3: lm>
## 2 Africa Angola <tibble [12 × 5]> <S3: lm>
## 3 Africa Benin <tibble [12 × 5]> <S3: lm>
## 4 Africa Botswana <tibble [12 × 5]> <S3: lm>
## 5 Africa Burkina Faso <tibble [12 × 5]> <S3: lm>
## 6 Africa Burundi <tibble [12 × 5]> <S3: lm>
## 7 Africa Cameroon <tibble [12 × 5]> <S3: lm>
## 8 Africa Central African Republic <tibble [12 × 5]> <S3: lm>
## 9 Africa Chad <tibble [12 × 5]> <S3: lm>
## 10 Africa Comoros <tibble [12 × 5]> <S3: lm>
## # ... with 42 more rows
models$model[[1]]
##
## Call:
## lm(formula = lifeExp ~ year1950, data = df)
##
## Coefficients:
## (Intercept) year1950
## 29.3566 0.2753
models$country[[1]]
## [1] Afghanistan
## 142 Levels: Afghanistan Albania Algeria Angola Argentina ... Zimbabwe
# Put it all together -----------------------------------------------------
by_country <- gapminder %>%
group_by(continent, country) %>%
nest() %>%
mutate(
model = map(data, country_model)
)
by_country
## # A tibble: 142 × 4
## continent country data model
## <fctr> <fctr> <list> <list>
## 1 Asia Afghanistan <tibble [12 × 5]> <S3: lm>
## 2 Europe Albania <tibble [12 × 5]> <S3: lm>
## 3 Africa Algeria <tibble [12 × 5]> <S3: lm>
## 4 Africa Angola <tibble [12 × 5]> <S3: lm>
## 5 Americas Argentina <tibble [12 × 5]> <S3: lm>
## 6 Oceania Australia <tibble [12 × 5]> <S3: lm>
## 7 Europe Austria <tibble [12 × 5]> <S3: lm>
## 8 Asia Bahrain <tibble [12 × 5]> <S3: lm>
## 9 Asia Bangladesh <tibble [12 × 5]> <S3: lm>
## 10 Europe Belgium <tibble [12 × 5]> <S3: lm>
## # ... with 132 more rows
# Broom for glance tidy and augment ---------------------------------------
models <- models %>%
mutate(
glance = map(model, broom::glance),
rsq = glance %>% map_dbl("r.squared"),
tidy = map(model, broom::tidy),
augment = map(model, broom::augment)
)
models
## # A tibble: 142 × 8
## continent country data model glance
## <fctr> <fctr> <list> <list> <list>
## 1 Asia Afghanistan <tibble [12 × 5]> <S3: lm> <data.frame [1 × 11]>
## 2 Europe Albania <tibble [12 × 5]> <S3: lm> <data.frame [1 × 11]>
## 3 Africa Algeria <tibble [12 × 5]> <S3: lm> <data.frame [1 × 11]>
## 4 Africa Angola <tibble [12 × 5]> <S3: lm> <data.frame [1 × 11]>
## 5 Americas Argentina <tibble [12 × 5]> <S3: lm> <data.frame [1 × 11]>
## 6 Oceania Australia <tibble [12 × 5]> <S3: lm> <data.frame [1 × 11]>
## 7 Europe Austria <tibble [12 × 5]> <S3: lm> <data.frame [1 × 11]>
## 8 Asia Bahrain <tibble [12 × 5]> <S3: lm> <data.frame [1 × 11]>
## 9 Asia Bangladesh <tibble [12 × 5]> <S3: lm> <data.frame [1 × 11]>
## 10 Europe Belgium <tibble [12 × 5]> <S3: lm> <data.frame [1 × 11]>
## # ... with 132 more rows, and 3 more variables: rsq <dbl>, tidy <list>,
## # augment <list>
models$glance[[1]]
## r.squared adj.r.squared sigma statistic p.value df logLik
## 1 0.9477123 0.9424835 1.222788 181.2494 9.835213e-08 2 -18.34693
## AIC BIC deviance df.residual
## 1 42.69387 44.14859 14.9521 10
models$tidy[[1]]
## term estimate std.error statistic p.value
## 1 (Intercept) 29.3566375 0.69898128 41.99918 1.404235e-12
## 2 year1950 0.2753287 0.02045093 13.46289 9.835213e-08
models$augment[[1]]
## lifeExp year1950 .fitted .se.fit .resid .hat .sigma
## 1 28.801 2 29.90729 0.6639995 -1.10629487 0.29487179 1.211813
## 2 30.332 7 31.28394 0.5799442 -0.95193823 0.22494172 1.237512
## 3 31.997 12 32.66058 0.5026799 -0.66358159 0.16899767 1.265886
## 4 34.020 17 34.03722 0.4358337 -0.01722494 0.12703963 1.288917
## 5 36.088 22 35.41387 0.3848726 0.67413170 0.09906760 1.267003
## 6 38.438 27 36.79051 0.3566719 1.64748834 0.08508159 1.154002
## 7 39.854 32 38.16716 0.3566719 1.68684499 0.08508159 1.147076
## 8 40.822 37 39.54380 0.3848726 1.27820163 0.09906760 1.208243
## 9 41.674 42 40.92044 0.4358337 0.75355828 0.12703963 1.260583
## 10 41.763 47 42.29709 0.5026799 -0.53408508 0.16899767 1.274051
## 11 42.129 52 43.67373 0.5799442 -1.54472844 0.22494172 1.148593
## 12 43.828 57 45.05037 0.6639995 -1.22237179 0.29487179 1.194109
## .cooksd .std.resid
## 1 2.427205e-01 -1.07742164
## 2 1.134714e-01 -0.88428127
## 3 3.603567e-02 -0.59530844
## 4 1.653992e-05 -0.01507681
## 5 1.854831e-02 0.58082792
## 6 9.225358e-02 1.40857509
## 7 9.671389e-02 1.44222437
## 8 6.668277e-02 1.10129103
## 9 3.165567e-02 0.65958143
## 10 2.334344e-02 -0.47913530
## 11 2.987950e-01 -1.43494020
## 12 2.963271e-01 -1.19046907
but maybe the plot can be clearer?
models %>%
ggplot(aes(rsq, country)) +
geom_point(aes(colour = continent))
# source("gapminderShiny.R")
ggplot orders categorical variables alphabetically
models %>%
ggplot(aes(rsq, reorder(country, rsq))) +
geom_point(aes(colour = continent))
models %>% filter((rsq<0.1 & rsq>0)) %>% unnest(rsq) %>% top_n(6,rsq) %>% unnest(data) %>%
ggplot(aes(year, lifeExp)) +
geom_line(aes( alpha = 1/3)) +
facet_wrap(~country)
# Unnest data -------------------------------------------------------------
unnest(models, data)
## # A tibble: 1,704 × 8
## continent country rsq year lifeExp pop gdpPercap
## <fctr> <fctr> <dbl> <int> <dbl> <int> <dbl>
## 1 Asia Afghanistan 0.9477123 1952 28.801 8425333 779.4453
## 2 Asia Afghanistan 0.9477123 1957 30.332 9240934 820.8530
## 3 Asia Afghanistan 0.9477123 1962 31.997 10267083 853.1007
## 4 Asia Afghanistan 0.9477123 1967 34.020 11537966 836.1971
## 5 Asia Afghanistan 0.9477123 1972 36.088 13079460 739.9811
## 6 Asia Afghanistan 0.9477123 1977 38.438 14880372 786.1134
## 7 Asia Afghanistan 0.9477123 1982 39.854 12881816 978.0114
## 8 Asia Afghanistan 0.9477123 1987 40.822 13867957 852.3959
## 9 Asia Afghanistan 0.9477123 1992 41.674 16317921 649.3414
## 10 Asia Afghanistan 0.9477123 1997 41.763 22227415 635.3414
## # ... with 1,694 more rows, and 1 more variables: year1950 <dbl>
unnest(models, glance, .drop = TRUE)
## # A tibble: 142 × 14
## continent country rsq r.squared adj.r.squared sigma
## <fctr> <fctr> <dbl> <dbl> <dbl> <dbl>
## 1 Asia Afghanistan 0.9477123 0.9477123 0.9424835 1.2227880
## 2 Europe Albania 0.9105778 0.9105778 0.9016355 1.9830615
## 3 Africa Algeria 0.9851172 0.9851172 0.9836289 1.3230064
## 4 Africa Angola 0.8878146 0.8878146 0.8765961 1.4070091
## 5 Americas Argentina 0.9955681 0.9955681 0.9951249 0.2923072
## 6 Oceania Australia 0.9796477 0.9796477 0.9776125 0.6206086
## 7 Europe Austria 0.9921340 0.9921340 0.9913474 0.4074094
## 8 Asia Bahrain 0.9667398 0.9667398 0.9634138 1.6395865
## 9 Asia Bangladesh 0.9893609 0.9893609 0.9882970 0.9766908
## 10 Europe Belgium 0.9945406 0.9945406 0.9939946 0.2929025
## # ... with 132 more rows, and 8 more variables: statistic <dbl>,
## # p.value <dbl>, df <int>, logLik <dbl>, AIC <dbl>, BIC <dbl>,
## # deviance <dbl>, df.residual <int>
unnest(models, tidy)
## # A tibble: 284 × 8
## continent country rsq term estimate std.error
## <fctr> <fctr> <dbl> <chr> <dbl> <dbl>
## 1 Asia Afghanistan 0.9477123 (Intercept) 29.3566375 0.698981278
## 2 Asia Afghanistan 0.9477123 year1950 0.2753287 0.020450934
## 3 Europe Albania 0.9105778 (Intercept) 58.5597618 1.133575812
## 4 Europe Albania 0.9105778 year1950 0.3346832 0.033166387
## 5 Africa Algeria 0.9851172 (Intercept) 42.2364149 0.756269040
## 6 Africa Algeria 0.9851172 year1950 0.5692797 0.022127070
## 7 Africa Angola 0.8878146 (Intercept) 31.7079741 0.804287463
## 8 Africa Angola 0.8878146 year1950 0.2093399 0.023532003
## 9 Americas Argentina 0.9955681 (Intercept) 62.2250191 0.167091314
## 10 Americas Argentina 0.9955681 year1950 0.2317084 0.004888791
## # ... with 274 more rows, and 2 more variables: statistic <dbl>,
## # p.value <dbl>
# Plot data frame ---------------------------------------------------------
plotLife <- models %>%
unnest(tidy) %>%
select(continent, country, term, estimate, rsq) %>%
spread(term, estimate) %>%
ggplot(aes(`(Intercept)`,year1950))+
geom_point(aes(colour = continent, size = rsq, fill = country)) +
geom_smooth(se=FALSE) +
xlab("Life expectancy (1950)") +
ylab("Yearly improvement") +
scale_size_area() + guides(fill=FALSE)
ggplotly(plotLife, tooltip = c("year1950", "country"))
## `geom_smooth()` using method = 'loess'
unnest(models, augment)
## # A tibble: 1,704 × 12
## continent country rsq lifeExp year1950 .fitted .se.fit
## <fctr> <fctr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Asia Afghanistan 0.9477123 28.801 2 29.90729 0.6639995
## 2 Asia Afghanistan 0.9477123 30.332 7 31.28394 0.5799442
## 3 Asia Afghanistan 0.9477123 31.997 12 32.66058 0.5026799
## 4 Asia Afghanistan 0.9477123 34.020 17 34.03722 0.4358337
## 5 Asia Afghanistan 0.9477123 36.088 22 35.41387 0.3848726
## 6 Asia Afghanistan 0.9477123 38.438 27 36.79051 0.3566719
## 7 Asia Afghanistan 0.9477123 39.854 32 38.16716 0.3566719
## 8 Asia Afghanistan 0.9477123 40.822 37 39.54380 0.3848726
## 9 Asia Afghanistan 0.9477123 41.674 42 40.92044 0.4358337
## 10 Asia Afghanistan 0.9477123 41.763 47 42.29709 0.5026799
## # ... with 1,694 more rows, and 5 more variables: .resid <dbl>,
## # .hat <dbl>, .sigma <dbl>, .cooksd <dbl>, .std.resid <dbl>
models %>% unnest(augment) %>%
ggplot(aes(year1950, .resid)) +
geom_line(aes(group = country), alpha = 1/3) +
geom_smooth(se = FALSE) +
geom_hline(yintercept = 0, colour = "white") +
facet_wrap(~continent)
## `geom_smooth()` using method = 'loess'
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